Transcriptom and miRNA data of PUFA-enriched stimulated murine macrophage and human endothelial cell lines

Inflammation is associated with the adaptation of macrophages and endothelial cells, and the dysregulation of these differentiation processes has been directly linked to both acute and chronic disease states. As cells in constant contact with blood, macrophages and endothelial cells are also under the direct influence of immunomodulatory dietary components such as polyunsaturated fatty acids (PUFA). RNA sequencing analyses allow us to understand the global changes in gene expression occurring during cell differentiation, including both transcriptional (transcriptome) and post-transcriptional (miRNAs) levels. We generated a comprehensive RNA sequencing dataset of parallel transcriptome and miRNA profiles of PUFA-enriched and pro-inflammatory stimulated macrophages and endothelial cells aiming to uncover the underlying molecular mechanisms. PUFA concentrations and duration of supplementation were based on dietary ranges, allowing for metabolism and plasma membrane uptake of fatty acids. The dataset may serve as a resource to study transcriptional and post-transcriptional changes associated with macrophage polarisation and endothelial dysfunction in inflammatory settings and their modulation by omega-3 and omega-6 fatty acids.


Background & Summary
Macrophages and endothelial cells are key cellular mediators of innate immune defence. Macrophages are directly activated by contact with bacterial surface structures, such as lipopolysaccharide (LPS; Gram-negative bacteria) or lipoteichoic acid (LTA; Gram-positive bacteria), initiating macrophage differentiation. There is a continuum of states of pro-inflammatory activation, often simplified as a differentiation to the pro-inflammatory M1 type 1 . Activation of endothelial cells is mediated by pro-inflammatory cytokines such as interleukin-1beta (IL-1β), tumour necrosis factor-alpha (TNF-α), and interferon-gamma (IFN-γ), which are released by M1-type macrophages 2 . The regulation of these processes is essential to combat pathogenic organisms and to maintain physical integrity. In fact, aberrant macrophage differentiation and endothelial dysfunction have been implicated in many acute and chronic diseases of inflammatory pathogenesis such as sepsis or artherosclerosis [3][4][5] .
Cellular differentiation and adaptive processes are accompanied by changes in protein expression. Inflammatory mediators initiate signal transduction cascades that ultimately influence the transcription of specific protein-coding genes. Besides bacterial surface structures and cytokines, polyunsaturated fatty acids (PUFA) influence gene expression in macrophages and endothelial cells 6 . Both cell types are particularly susceptible to dietary influences because they are in constant contact with the blood. Epidemiologic and interventional studies show protective effects of PUFA against adverse cardiovascular events, reduction of arterial stiffness and vascular inflammatory processes, and even improvements in sepsis patients 7 . Interestingly, PUFA may be modulators of gene expression through multiple pathways. Described mechanisms of action include (1) binding of PUFA to the immune cell receptors peroxisome proliferator-activated receptor gamma (PPARγ) and G protein-coupled receptor 120 (GPR120), (2) conversion of PUFA to eicosanoids and resolvins, which are potent immune mediators, and (3) incorporation of PUFA into the lipid raft domains of the plasma membrane, thereby affecting protein-protein interactions of membrane receptors 8 .
Besides transcriptional regulation, mRNA copy number is fine-tuned post-transcriptionally 9 . This is mediated by small non-coding RNAs, so-called miRNAs. miRNAs interact with mRNAs via partial complementarity, negatively affecting the stability and translational efficiency of targeted mRNAs 9 . Thus, a comprehensive understanding of protein expression requires a concurrent analysis of transcriptome and miRNAs. An efficient approach to global transcriptome and miRNA profiling is next-generation sequencing (NGS). It allows hypothesis-free, genome-wide analysis of mRNA and miRNA expression and their bioinformatics evaluation.
Genomics data is one of the fastest growing areas of big data. However, parallel transcriptome and miRNA profiling is still rare. This is especially true for analysing PUFA effects in the immunological setting. In this data descriptor, we aimed to provide both transcriptomic and miRNA data sets from inflammation-prone macrophages and endothelial cells supplemented with PUFA in a defined manner. As shown in the study design ( Fig. 1), (1) naive versus LPS-or LTA-stimulated macrophages and (2) unstimulated versus cytokine-stimulated endothelial cells were analysed. The influence of the PUFA docosahexaenoic acid (DHA, an omega-3 fatty acid) and arachidonic acid (AA, an omega-6 fatty acid) was investigated by considering all possible combinations of fatty acid supplementation and stimulation. The duration of PUFA supplementation was chosen to allow incorporation of the fatty acids into the plasma membrane to reach a membrane steady state [10][11][12] . It is emphasized that supplementation was performed at a physiologically relevant concentration 13 . Preliminary work of the group shows that such PUFA enrichment of macrophage membrane interferes with the interaction of LPS-inducible Toll-like receptor 4 (TLR4) with its co-receptor CD14, which also favours macrophage differentiation to the anti-inflammatory M2 type in inflammatory settings [14][15][16][17] . PUFA administration also has anti-inflammatory effects in endothelial cells. Both stimulus-induced synthesis and release of inflammatory cytokines and the expression of adhesion proteins, which are important for macrophage trans-endothelial migration, are reduced in endothelial cells enriched in PUFA 12 . Therefore, the data set generated here may help other (nutritional) scientists and clinicians to gain new insights into how PUFA act. This dataset is intended to provide a useful and reliable tool to elucidate molecular mechanisms of PUFA modulation of macrophage/endothelial cell activation at both transcriptional and post-transcriptional levels. Two key representatives of omega-3 and omega-6 fatty acids, DHA and AA, were specifically selected to allow parallel and comparative studies of the effects of PUFA of these two subclasses.

Methods
Cell culture. Two commercially available cell lines were used: the murine macrophage cell line RAW264.7 (ATCC number: TIB-71) and the human telomerase-immortalized microvascular endothelial cell line TIME (ATCC number: CRL-4025). To ensure cell line integrity, both cell lines were purchased directly from LGC Standards, Wesel, Germany and the cell culture was regularly checked for mycoplasma contamination by PCR according to the recommendations of the Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures.
For cellular fatty acid enrichment RAW264.7 as well as TIME were incubated in cell culture medium supplemented with either docosahexaenoic acid (DHA, C22:6n3) or arachidonic acid (AA, C20:4n6) (both Biotrend, Köln, Germany) in concentrations of 15 μmol/l using ethanol as a vehicle (0.2% v/v final ethanol concentration). Cells were cultured in the enriched media in 75 cm 2 cell culture flasks totaling either 72 h (RAW264.7) or 144 h (TIME) at 37 °C and 5% CO 2 in a humidified atmosphere.
For stimulation RAW264.7 were treated with either 1 μg/ml LPS (from Escherichia coli serotype 0111:B4) or 0.5 µg/ml LTA (from Staphylococcus aureus) (Sigma-Aldrich, Taufkirchen, Germany) and TIME were treated with a cytokine mix consisting of IL-1β, TNF-α and IFN-γ, each in a concentration of 5 ng/ml (all PeproTech, Hamburg, Germany). Stimulation was performed in the last 24 hours of fatty acid supplementation.
Periods of supplementation and stimulation were proven to result in a membrane fatty acid steady state as well as reproducible effects on macrophage/endothelial cell functionality 10-12,15-17 . total RNa isolation. Total RNA extraction was performed using a standard liquid-liquid extraction protocol based on TRIzol LS (Thermo Fisher Scientific, Dreieich, Germany) according to the manufacturer's instructions. The concentration and quality of RNA gained were analyzed by means of the NanoDrop spectrophotometer (Thermo Fisher Scientific, Dreieich, Germany) as well as the Agilent Bioanalyzer (Agilent Technologies, Waldbronn, Germany). Processing of RNA-seq data was performed by the Core Facility Imaging, University Medicine Halle (Saale) for Novogene-analyzed samples and the Core Unit DNA, Leipzig University, respectively. The following steps were taken by the Core Facility Imaging, University Medicine Halle (Saale): Low quality read ends as well as www.nature.com/scientificdata www.nature.com/scientificdata/ remaining parts of sequencing adapters were clipped off using Cutadapt (v 1.14) with parameters -q 20 -O 7 -m 20. Trimmed reads were mapped against the mouse genome (mm10 UCSC) for cell line RAW264.7 or human genome (hg38 UCSC) for cell line TIME using (i) Bowtie2 (v 2.3.2) with parameters -N 1 for small RNA and (ii) HiSat2 (v 2.1.0) with parameters -p 6-dta-strandness RF -k 5 for poly-A-RNA, respectively. Secondary www.nature.com/scientificdata www.nature.com/scientificdata/ alignments were filtered out using samtools (v 1.5). Mapped reads were summarized using featureCounts (v 1.5.3). TMM normalisation was done using the R/Bioconductor package EdgeR. The following steps were carried out by the Core Unit DNA, Leipzig University: Basecalls and demultiplexing was performed using CASAVA version 1.4. Raw reads were adapter trimmed with cutadapt version 1.9.1. Only adapter trimmed reads between 15 and 27 bp lenght were considered processed miRNAs and selected for alignment. The small RNA seq reads were aligned to the mouse genome (mm10 UCSC) for cell line RAW264.7 or human genome (hg38 UCSC) for cell line TIME using Bowtie2 version 2.2.7 allowing 1 mismatch and alignment to multiple targets. Reads were annotated by intersecting genome coordinates of known miRNAs from miRBase version 21 using Bedtools version 2.25.0. Reads were counted using the R/Bioconductor programming environment by application of the ShortRead library and the table function together with the miRNA name in the annotation. Count normalisation was done using the R/Bioconductor packages DESeq 2 and EdgeR.
All generated RNA-seq data were deposited at the Gene Expression Omnibus (GEO) repository.
RNa-seq data analyses. RNA-seq data were analyzed for differential gene expression, focusing on altered expressed miRNAs. Gene expression profiling was performed by the Core Facility Imaging, University Medicine Halle (Saale) for Novogene-analyzed samples and the Core Unit DNA, Leipzig University, respectively. Identified miRNA candidates were validated by Droplet Digital PCR (ddPCR) technology (BioRad, Munich, Germany). ddPCR allows determination of the absolute copy number of a miRNA per total amount of RNA in the sample. The need to refer to an (presumed) stably expressed gene, the so-called housekeeper, is omitted. This is particularly important in the inflammatory setting, as frequently used housekeepers, such as U6, have been shown to reveal changes in expression profile under inflammatory conditions making them non-suitable [18][19][20] .
A more detailed description of each of these analyses, as well as the results obtained, have been published in several articles [25][26][27][28][29] .